In [1]}, a GFlowNet is described by a parametric estimate of the edge flow function, \(F(u{\rightarrow }v;\theta )\)  (a neural net with parameters \(\theta \) ). These conditions can be converted into objectives that are minimized when (REF ) is satisfied. For example, the flow-matching objective at a nonterminal state \(s\)  is defined by
\(\mathcal {L}_{\rm FM}(s)=\left(\log \frac{\sum _{s:(s{\rightarrow }t)\in \mathcal {A}}F(s{\rightarrow }t;\theta )+\epsilon }{\sum _{u:(t{\rightarrow }u)\in \mathcal {A}}F(t{\rightarrow }u;\theta )+\epsilon }\right)^2,\) 
